no code implementations • 4 May 2024 • Srijit Seal, Maria-Anna Trapotsi, Ola Spjuth, Shantanu Singh, Jordi Carreras-Puigvert, Nigel Greene, Andreas Bender, Anne E. Carpenter
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions.
no code implementations • 19 Mar 2024 • Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains.
no code implementations • 6 Mar 2024 • Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya K Joshi, Pietro Liò, Shantanu Singh, Andreas Bender, Pietro Sormanni, Matthew Greenig
Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models.
no code implementations • 23 Oct 2023 • Roman Hornung, Malte Nalenz, Lennart Schneider, Andreas Bender, Ludwig Bothmann, Bernd Bischl, Thomas Augustin, Anne-Laure Boulesteix
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
1 code implementation • 24 May 2023 • Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender
The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data.
no code implementations • 17 Oct 2022 • Miguel Garcia-Ortegon, Andreas Bender, Sergio Bacallado
Neural processes (NPs) are models for transfer learning with properties reminiscent of Gaussian Processes (GPs).
no code implementations • 6 Oct 2022 • Susanne Dandl, Andreas Bender, Torsten Hothorn
Most importantly, the noncollapsibility issue necessitates the joint estimation of treatment and prognostic effects.
no code implementations • 25 May 2022 • David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl
Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models.
no code implementations • 12 Feb 2022 • Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer
Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications.
1 code implementation • 9 Dec 2021 • Raphael Sonabend, Andreas Bender, Sebastian Vollmer
In this paper we consider how to evaluate survival distribution predictions with measures of discrimination.
1 code implementation • 29 Oct 2021 • Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado
The field of machine learning for drug discovery is witnessing an explosion of novel methods.
2 code implementations • 19 Feb 2021 • Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, William L Hamilton
We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources.
no code implementations • 11 Nov 2020 • Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning.
no code implementations • 18 Aug 2020 • Raphael Sonabend, Franz J. Király, Andreas Bender, Bernd Bischl, Michel Lang
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models.
1 code implementation • 27 Jun 2020 • Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events.
no code implementations • 9 Aug 2019 • Isidro Cortés-Ciriano, Andreas Bender
Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings.
no code implementations • 12 Apr 2019 • Isidro Cortes-Ciriano, Andreas Bender
Here, we present a framework to compute reliable errors in prediction for Neural Networks using Test-Time Dropout and Conformal Prediction.
1 code implementation • 22 Nov 2018 • Isidro Cortes Ciriano, Andreas Bender
We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints.
no code implementations • 24 Sep 2018 • Isidro Cortes-Ciriano, Andreas Bender
While controlling for prediction confidence is essential to increase the trust, interpretability and usefulness of virtual screening models in drug discovery, techniques to estimate the reliability of the predictions generated with deep learning networks remain largely underexplored.
2 code implementations • 4 Jun 2018 • Andreas Bender, Fabian Scheipl
This article introduces the pammtools package, which facilitates data transformation, estimation and interpretation of Piece-wise exponential Additive Mixed Models.
Computation
1 code implementation • Bioinformatics 2017 • Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, Günter Klambauer
While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space.
no code implementations • 23 Nov 2014 • Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable.